CuPID: Leveraging Masked Single-Lead ECG Modelling for Enhancing the Representations

TL;DR

CuPID introduces a novel masked data modeling approach for single-lead ECGs by cueing spectrogram context, significantly improving representation quality and outperforming existing methods in various downstream health monitoring tasks.

Contribution

The paper proposes CuPID, a new MDM method that enhances ECG representations by cueing spectrogram context, addressing limitations of previous techniques for irregular heartbeat data.

Findings

CuPID outperforms state-of-the-art methods in downstream tasks.

Enhanced ECG representations lead to better health monitoring performance.

CuPID improves encoder performance across multiple configurations.

Abstract

Wearable sensing devices, such as Electrocardiogram (ECG) heart-rate monitors, will play a crucial role in the future of digital health. This continuous monitoring leads to massive unlabeled data, incentivizing the development of unsupervised learning frameworks. While Masked Data Modelling (MDM) techniques have enjoyed wide use, their direct application to single-lead ECG data is suboptimal due to the decoder's difficulty handling irregular heartbeat intervals when no contextual information is provided. In this paper, we present Cueing the Predictor Increments the Detailing (CuPID), a novel MDM method tailored to single-lead ECGs. CuPID enhances existing MDM techniques by cueing spectrogram-derived context to the decoder, thus incentivizing the encoder to produce more detailed representations. This has a significant impact on the encoder's performance across a wide range of different configurations, leading CuPID to outperform state-of-the-art methods in a variety of downstream tasks.